Precision agriculture involves observing and responding to localized field conditions on a farm, vineyard or orchard using technologies such as aerial and satellite imagery, sensor arrays and geographic information systems, so as to optimize field-level management. The process involves matching the topology of a production area with the geo-location of data, such as climactic conditions, soil conditions, weeds and diseases, across the area to be managed.
The recording of localized data and its delivery to a database in so-called precision agriculture may be by means of a wireless sensor network. In practice, the successful implementation of sensor networks over the relatively large areas covered by a farm, vineyard or orchard has faced challenges and obstacles.
A network of sensors distributed throughout an agricultural production area must have access to sufficient power to operate over long periods with little and preferably no maintenance.
It has been contemplated to use wireless sensor mesh networks for data collection, for example using the ZigBee or other protocols.
In order to conserve energy and extend their standalone working life, sensors in wireless sensor mesh networks are sometimes operated using sleep and awake modes, awakening during specified time intervals to transmit data. The awake time must be long enough for the sensor to receive and forward any data that may be received from other sensors in the mesh network. This results in the relatively large number of sensor nodes in the network being awake and transmitting or retransmitting at substantially the same time. The inventors have found that this results in interference and collisions that must be managed and that degrade the performance of the network. An alternative that has been attempted by the inventors is to enlarge the time slots for mesh network transmissions between the sensors. However, any enlargement of the awake time is at the direct (and almost inversely proportional) expense of battery life.
An additional challenge in the use of wireless sensor mesh networks in agricultural applications is that the optimal location of the sensors is often on the trunk of the plant or within its foliage in which case the vegetation itself acts as an obstruction to signal propagation. This threatens to orphan some nodes in the mesh network or to require more retransmissions and impose a greater network management burden, again drawing more power from the battery. More significantly, placing a sensor unit in or beneath the foliage severely restricts the available insolation for solar panels, particularly in more challenging climates.
The foregoing considerations combine to severely compromise the ability of the sensors to operate according to a mesh network protocol in agricultural applications where there are many sensor nodes or they are deployed within or under the foliage.
Libelium Comunicaciones Distribuidas S. L. discloses (on its web site at libelium.com/smart_agriculture_vineyard_sensors_waspmote) the general features of a network-based system for the monitoring of environmental parameters in an agricultural production area. The system includes a plurality of sensor nodes operating wirelessly in any of several network protocols including ZigBee. The sensor nodes have sleep modes and hibernate modes. When active, they communicate with gateway nodes which are mounted on poles and connect to a remote server over the Internet through 3G/GPRS.
U.S. Pat. No. 8,026,822 to Borth et al. discloses the use of a wireless sensor network in a pest control application. Pest control devices communicate wirelessly, through repeater nodes if necessary, to a gateway that makes data available to a remote data server for viewing, analysis or messaging.
WO 2011/090938 to Rhee et al. discloses the use of a wireless mesh network in a rodent pest control system. Each of various traps is paired with a wireless device, the wireless devices being in communication via a mesh network for reporting the capture of an animal. A repeater may be used in the mesh to extend the effective range of the network. Data is routed through the mesh network to a wireless gateway to an external network and hence to a management server.
Neither Borth et al. or Rhee et al. discuss the issues involved in applying the invention to an agricultural context.
U.S. Pat. No. 7,916,951 to Landwehr et al. discloses an insect monitoring device for use in an agricultural context. Landwehr et al. refer generally to networking multiple devices and relaying information to a processor but no detail is provided in relation to the network operation or in relation to any problems arising from operating sensor mesh networks within crop foliage.
A specific opportunity exists to apply wireless sensor networking in the field of insect management in agricultural applications. The current practice in agricultural areas involves setting up an array of insect traps distributed across a field, farm, vineyard or orchard, weekly attendance at each trap for manual or automation-assisted logging of insect activity, resetting or cleaning the traps, reviewing the collected data and the subsequent decision whether to disperse pesticides or bio-pesticides in the production area. While the agricultural industry is moving toward fully organic farming by reducing pesticide usage, such a reduction requires precision control of insect infestations, which is economically challenging in view of the labor-intensive approach to insect detection.
Organic farming also requires using bio-pesticides that rely on artificial insect pheromones to disrupt the insect mating cycle. The pheromones are continuously dispersed using timed aerosol sprayers or time release compounds which can last most of a growing season. The effectiveness of the pheromones is determined by placing traps to catch, count and identify targeted insects on a weekly basis. The trap counts are relied on to adjust the placement of pheromone sprayers or the compounds used. More frequent and real-time monitoring would allow a quicker and more effective response to any pest conditions that might be detected, while the application of precision agriculture concepts may allow the response to be more localized.
The application of wide scale characterization and analysis of insect populations would also enable the more targeted application of pheromone-based disruptors, and the use of more measured minimal quantities than are conventional used at present. Wind, altitude, humidity, vegetation and topological features can determine pheromone drift. A real-time localized insect count opens the possibility of inferring the propagation pattern of pheromones for more efficient dispersal.
The present invention is directed to a monitoring and control system for agricultural applications that addresses transmission and network management problems arising from crop foliage.